An overview of Sequential Monte Carlo methods for parameter estimation in general state-space models

An overview of Sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, provide very good numerical approximations to the associated optimal state estimation problems. However, in many scenarios,...

書誌詳細
主要な著者:	Kantas, N, Doucet, A, Singh, S, MacIejowski, J
フォーマット:	Journal article
言語:	English
出版事項:	2009

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